Deep learning based methods have outperformed conventional approaches for wireless resource allocation in recent works [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}.
Broadly, there are two ways of incorporating deep learning in solving resource allocation problems.
One way is to follow a supervised or imitation learning approach where the functional approximation capacity of deep neural networks is leveraged to mimic established classical methods from solved instances [8]}.
For example, a deep network has been employed [9]} to approximate the classical WMMSE solution [10]} for the power allocation problem in an interference channel.
The main advantage of this paradigm is computational efficiency since, once trained, deep networks can run faster than the classical algorithms that they are imitating.
By contrast, the main drawback is the poor generalization to scenarios (channel characteristics, number of users, user mobility) not represented in the original training examples. While expanding the training set to be more inclusive of the diversity of such phenomena is desirable, it may be infeasible to obtain groundtruth for such a large and representative set as that would involve repeated application of the computationally heavy iterative algorithm.
The second approach is unsupervised where one parameterizes the power allocation function using a neural network and directly employs the optimization objective as a loss function, bypassing the need for solved problem instances [1]}, [12]}, [13]}, [14]}, [15]}. This loss function is minimized during training with respect to the parameters defining the neural network.
While such a procedure is computationally simple, no prior knowledge (e.g., based on classical optimization techniques) is leveraged to inform the algorithm's architectural or hyper-parameter choices.
